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Purposes The algoritihm of phasic policy gradient with sample reuse (SR-PPG) is proposed to address the problems of non-reuse of samples and low sample utilization in policybased deep reinforcement learning algorithms. Methods In the proposed algorithm, offline data are introduced on the basis of the phasic policy gradient (PPG), thus reducing the time cost of training and enabling the model to converge quickly. In this work, SR-PPG combines the stability advantages of theoretically supported on-policy algorithms with the sample efficiency of offpolicy algorithms to develop policy improvement guarantees applicable to off-policy settings and to link these bounds to the clipping mechanism used by PPG. Findings A series of theoretical and experimental demonstrations show that this algorithm provides better performance by effectively balancing the competing goals of stability and sample efficiency.